{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
       "       0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  ])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x_known = np.array([0.194, 0.429, 0.512, 0.821, 0.917])\n",
    "\n",
    "y_known = np.array([0.347, 0.543, 0.661, 0.811, 0.793])\n",
    "\n",
    "x_desired = np.linspace(0, 1, num=21)\n",
    "\n",
    "x_desired"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d\n",
    "new_fn = interp1d(x_known, y_known, \n",
    "                  kind='cubic', bounds_error=False, fill_value=np.NaN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_desired = new_fn(x_desired)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x_known, y_known)\n",
    "plt.plot(x_desired, y_desired)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
